# are the result of moving between the battle and such
table(is.na(survey_sf$West_Mosul))
# come back and check on this -- related to changing treatment based on movers; 11 obserations we are missing the coded nieghboorhood information on
survey_sf$treated <- survey_sf$West_Mosul
# # merge in neighboorhood level covariates
Neighborhood_number <- st_intersects(survey_sf, Neighborhood)
Neighborhood_number <- as.data.frame(Neighborhood_number)
colnames(Neighborhood_number)[2] <- "OBJECTID"
survey_sf$row.id <- 1:nrow(survey_sf)
survey_sf <- merge(survey_sf, Neighborhood_number, by = "row.id")
survey_sf
survey_sf %>%
select(SbjNum, OBJECTID)%>%
rename(OBJECTID = neighborhood_indicator2)
survey_sf %>%
select(SbjNum, OBJECTID)%>%
rename(neighborhood_indicator2 = OBJECTID)
as.dataframe(survey_sf) %>%
select(SbjNum, OBJECTID)%>%
rename(neighborhood_indicator2 = OBJECTID)
as.data.frame(survey_sf)
as.data.frame(survey_sf) %>%
select(SbjNum, OBJECTID)%>%
rename(neighborhood_indicator2 = OBJECTID)
indicator <- as.data.frame(survey_sf) %>%
select(SbjNum, OBJECTID)%>%
rename(neighborhood_indicator2 = OBJECTID)
write_csv(indicator, "/Users/benjaminkrick/Dropbox/Mosul Natural Experiment/REPOSITORY/data/raw_data/neighborhood_indicator.csv")
rm(list = ls())
library(sf)
library(osmdata)
library(ggplot2)
library(tidyverse)
library(readxl)
library(haven)
# Load Data ----------------------------------------------------------
dat0 <- read_csv("data/raw_data/mosul_survey.csv")
rm(list = ls())
library(sf)
library(osmdata)
library(ggplot2)
library(tidyverse)
library(readxl)
library(haven)
# Load Data ----------------------------------------------------------
dat0 <- read_csv("data/raw_data/mosul_survey.csv")
# merge in buffer damage (pre-processed based on respondent coordinates)
# UNITAR - UNOSAT
damage <- read_csv("data/processed_data/damage_buffer.csv")
dat0 <- merge(dat0, damage[c("SbjNum", "damage_in_10m", "damage_in_50m", "damage_in_100m", "damage_in_500m", "damage_in_1km")], by = "SbjNum")
# Fix people who moved ----------------------------------------------------------
# Ensure respondents:
# (1) Lived in Mosul during the battle
# (2) Did not move neighborhoods after the battle
# (3) Have treatment and covariates that reflect these conditions
# Remove respondents who did not live in Mosul during the campaign
# [Q12_1] "Were you living in Mosul for any part of the military campaign to expel Daesh?"
# 1 = Yes, 0 = No
# This restricts the sample to 1,282 respondents
dat1 <- dat0 %>%
mutate(sample_1 = ifelse(!Q12_1 %in% 0, 1, 0)) %>%
filter(sample_1 == 1)
# Fix issue with respondents who lived in a different house during the battle
# [Q12_2] "Were you living in this house/apartment during the battle?"
# 1 = This house, 2 = Different house
table(dat1$Q12_2)  # 858 stayed, 152 moved, some unknown
# [Q12_3] If different house, which neighborhood were you in during the battle?
# Replace invalid neighborhood codes with NA
dat1$Q12_3 <- ifelse(dat1$Q12_3 %in% c(96, 208), NA, dat1$Q12_3)
# Correct misclassified neighborhoods
dat1$Q12_3 <- recode(dat1$Q12_3, `183` = 182, `185` = 184, `190` = 189)
# Update neighborhood for those who moved
dat1$neighborhood_indicator <- ifelse(dat1$Q12_2 == 2, dat1$Q12_3, dat1$Neighborhood)
# Replace damage values for those who moved using neighborhood-level means
dat1 <- dat1 %>%
group_by(neighborhood_indicator) %>%
mutate(mean_damage_in_10m = mean(damage_in_10m),
mean_damage_in_50m = mean(damage_in_50m),
mean_damage_in_100m = mean(damage_in_100m))
dat1$damage_in_10m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_10m, dat1$damage_in_10m)
dat1$damage_in_50m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_50m, dat1$damage_in_50m)
dat1$damage_in_100m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_100m, dat1$damage_in_100m)
# Define Treatment ----------------------------------------------------------
# Load neighborhood location data (East/West of river)
EastWest_Indicated <- read_csv("data/raw_data/neighborhood_side.csv") %>%
mutate(West_Mosul = ifelse(location == "West", 1, 0)) %>%
select(neighborhood_indicator, neighborhood_indicator2, West_Mosul)
rm(list = ls())
library(sf)
library(osmdata)
library(ggplot2)
library(tidyverse)
library(readxl)
library(haven)
# Load Data ----------------------------------------------------------
dat0 <- read_csv("data/raw_data/mosul_survey.csv")
# merge in buffer damage (pre-processed based on respondent coordinates)
# UNITAR - UNOSAT
damage <- read_csv("data/processed_data/damage_buffer.csv")
dat0 <- merge(dat0, damage[c("SbjNum", "damage_in_10m", "damage_in_50m", "damage_in_100m", "damage_in_500m", "damage_in_1km")], by = "SbjNum")
# Fix people who moved ----------------------------------------------------------
# Ensure respondents:
# (1) Lived in Mosul during the battle
# (2) Did not move neighborhoods after the battle
# (3) Have treatment and covariates that reflect these conditions
# Remove respondents who did not live in Mosul during the campaign
# [Q12_1] "Were you living in Mosul for any part of the military campaign to expel Daesh?"
# 1 = Yes, 0 = No
# This restricts the sample to 1,282 respondents
dat1 <- dat0 %>%
mutate(sample_1 = ifelse(!Q12_1 %in% 0, 1, 0)) %>%
filter(sample_1 == 1)
# Fix issue with respondents who lived in a different house during the battle
# [Q12_2] "Were you living in this house/apartment during the battle?"
# 1 = This house, 2 = Different house
table(dat1$Q12_2)  # 858 stayed, 152 moved, some unknown
# [Q12_3] If different house, which neighborhood were you in during the battle?
# Replace invalid neighborhood codes with NA
dat1$Q12_3 <- ifelse(dat1$Q12_3 %in% c(96, 208), NA, dat1$Q12_3)
# Correct misclassified neighborhoods
dat1$Q12_3 <- recode(dat1$Q12_3, `183` = 182, `185` = 184, `190` = 189)
# Update neighborhood for those who moved
dat1$neighborhood_indicator <- ifelse(dat1$Q12_2 == 2, dat1$Q12_3, dat1$Neighborhood)
# Replace damage values for those who moved using neighborhood-level means
dat1 <- dat1 %>%
group_by(neighborhood_indicator) %>%
mutate(mean_damage_in_10m = mean(damage_in_10m),
mean_damage_in_50m = mean(damage_in_50m),
mean_damage_in_100m = mean(damage_in_100m))
dat1$damage_in_10m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_10m, dat1$damage_in_10m)
dat1$damage_in_50m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_50m, dat1$damage_in_50m)
dat1$damage_in_100m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_100m, dat1$damage_in_100m)
# Define Treatment ----------------------------------------------------------
# Load neighborhood location data (East/West of river)
EastWest_Indicated <- read_csv("data/raw_data/neighborhood_side.csv") %>%
mutate(West_Mosul = ifelse(location == "West", 1, 0)) %>%
select(neighborhood_indicator, West_Mosul)
# merge dataset with neighborhood into dataset
dat2 <- merge(dat1, EastWest_Indicated, by = "neighborhood_indicator", all.x = T)
# merge additional neighborhood indicator into dataset
id <- read_csv("data/raw_data/neighborhood_indicator.csv")
dat2 <- merge(dat1, id, by = "SbjNumber", all.x = T)
dat2 <- merge(dat1, id, by = "SbjNum", all.x = T)
View(dat2)
# Assign treatment based on West_Mosul indicator
dat2 <- dat2 %>%
mutate(treated = West_Mosul)
rm(list = ls())
library(sf)
library(osmdata)
library(ggplot2)
library(tidyverse)
library(readxl)
library(haven)
# Load Data ----------------------------------------------------------
dat0 <- read_csv("data/raw_data/mosul_survey.csv")
# merge in buffer damage (pre-processed based on respondent coordinates)
# UNITAR - UNOSAT
damage <- read_csv("data/processed_data/damage_buffer.csv")
dat0 <- merge(dat0, damage[c("SbjNum", "damage_in_10m", "damage_in_50m", "damage_in_100m", "damage_in_500m", "damage_in_1km")], by = "SbjNum")
# Fix people who moved ----------------------------------------------------------
# Ensure respondents:
# (1) Lived in Mosul during the battle
# (2) Did not move neighborhoods after the battle
# (3) Have treatment and covariates that reflect these conditions
# Remove respondents who did not live in Mosul during the campaign
# [Q12_1] "Were you living in Mosul for any part of the military campaign to expel Daesh?"
# 1 = Yes, 0 = No
# This restricts the sample to 1,282 respondents
dat1 <- dat0 %>%
mutate(sample_1 = ifelse(!Q12_1 %in% 0, 1, 0)) %>%
filter(sample_1 == 1)
# Fix issue with respondents who lived in a different house during the battle
# [Q12_2] "Were you living in this house/apartment during the battle?"
# 1 = This house, 2 = Different house
table(dat1$Q12_2)  # 858 stayed, 152 moved, some unknown
# [Q12_3] If different house, which neighborhood were you in during the battle?
# Replace invalid neighborhood codes with NA
dat1$Q12_3 <- ifelse(dat1$Q12_3 %in% c(96, 208), NA, dat1$Q12_3)
# Correct misclassified neighborhoods
dat1$Q12_3 <- recode(dat1$Q12_3, `183` = 182, `185` = 184, `190` = 189)
# Update neighborhood for those who moved
dat1$neighborhood_indicator <- ifelse(dat1$Q12_2 == 2, dat1$Q12_3, dat1$Neighborhood)
# Replace damage values for those who moved using neighborhood-level means
dat1 <- dat1 %>%
group_by(neighborhood_indicator) %>%
mutate(mean_damage_in_10m = mean(damage_in_10m),
mean_damage_in_50m = mean(damage_in_50m),
mean_damage_in_100m = mean(damage_in_100m))
dat1$damage_in_10m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_10m, dat1$damage_in_10m)
dat1$damage_in_50m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_50m, dat1$damage_in_50m)
dat1$damage_in_100m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_100m, dat1$damage_in_100m)
# Define Treatment ----------------------------------------------------------
# Load neighborhood location data (East/West of river)
EastWest_Indicated <- read_csv("data/raw_data/neighborhood_side.csv") %>%
mutate(West_Mosul = ifelse(location == "West", 1, 0)) %>%
select(neighborhood_indicator, West_Mosul)
# merge dataset with neighborhood into dataset
dat2 <- merge(dat1, EastWest_Indicated, by = "neighborhood_indicator", all.x = T)
# merge additional neighborhood indicator into dataset
id <- read_csv("data/raw_data/neighborhood_indicator.csv")
dat2 <- merge(dat1, id, by = "SbjNum", all.x = T)
# Assign treatment based on West_Mosul indicator
dat2 <- dat2 %>%
mutate(treated = West_Mosul)
rm(list = ls())
library(sf)
library(osmdata)
library(ggplot2)
library(tidyverse)
library(readxl)
library(haven)
# Load Data ----------------------------------------------------------
dat0 <- read_csv("data/raw_data/mosul_survey.csv")
# merge in buffer damage (pre-processed based on respondent coordinates)
# UNITAR - UNOSAT
damage <- read_csv("data/processed_data/damage_buffer.csv")
dat0 <- merge(dat0, damage[c("SbjNum", "damage_in_10m", "damage_in_50m", "damage_in_100m", "damage_in_500m", "damage_in_1km")], by = "SbjNum")
# Fix people who moved ----------------------------------------------------------
# Ensure respondents:
# (1) Lived in Mosul during the battle
# (2) Did not move neighborhoods after the battle
# (3) Have treatment and covariates that reflect these conditions
# Remove respondents who did not live in Mosul during the campaign
# [Q12_1] "Were you living in Mosul for any part of the military campaign to expel Daesh?"
# 1 = Yes, 0 = No
# This restricts the sample to 1,282 respondents
dat1 <- dat0 %>%
mutate(sample_1 = ifelse(!Q12_1 %in% 0, 1, 0)) %>%
filter(sample_1 == 1)
# Fix issue with respondents who lived in a different house during the battle
# [Q12_2] "Were you living in this house/apartment during the battle?"
# 1 = This house, 2 = Different house
table(dat1$Q12_2)  # 858 stayed, 152 moved, some unknown
# [Q12_3] If different house, which neighborhood were you in during the battle?
# Replace invalid neighborhood codes with NA
dat1$Q12_3 <- ifelse(dat1$Q12_3 %in% c(96, 208), NA, dat1$Q12_3)
# Correct misclassified neighborhoods
dat1$Q12_3 <- recode(dat1$Q12_3, `183` = 182, `185` = 184, `190` = 189)
# Update neighborhood for those who moved
dat1$neighborhood_indicator <- ifelse(dat1$Q12_2 == 2, dat1$Q12_3, dat1$Neighborhood)
# Replace damage values for those who moved using neighborhood-level means
dat1 <- dat1 %>%
group_by(neighborhood_indicator) %>%
mutate(mean_damage_in_10m = mean(damage_in_10m),
mean_damage_in_50m = mean(damage_in_50m),
mean_damage_in_100m = mean(damage_in_100m))
dat1$damage_in_10m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_10m, dat1$damage_in_10m)
dat1$damage_in_50m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_50m, dat1$damage_in_50m)
dat1$damage_in_100m <- ifelse(dat1$Q12_2 == 2, dat1$mean_damage_in_100m, dat1$damage_in_100m)
# Define Treatment ----------------------------------------------------------
# Load neighborhood location data (East/West of river)
EastWest_Indicated <- read_csv("data/raw_data/neighborhood_side.csv") %>%
mutate(West_Mosul = ifelse(location == "West", 1, 0)) %>%
select(neighborhood_indicator, West_Mosul)
# merge dataset with neighborhood into dataset
dat2 <- merge(dat1, EastWest_Indicated, by = "neighborhood_indicator", all.x = T)
# merge additional neighborhood indicator into dataset
id <- read_csv("data/raw_data/neighborhood_indicator.csv")
dat2 <- merge(dat2, id, by = "SbjNum", all.x = T)
# Assign treatment based on West_Mosul indicator
dat2 <- dat2 %>%
mutate(treated = West_Mosul)
# Merge in neighborhood level covariates
Neighborhood <- as.data.frame(st_read("data/raw_data/UN_Habitat_Mosul_Neighborhoods/Neighborhood.shp"))
Neighborhood <- Neighborhood %>%
select(c("OBJECTID", "POPULATION", "RES_UNITS", "Shape__Are")) %>%
rename(neighborhood_indicator2 = OBJECTID,
area = Shape__Are)
dat2 <- left_join(dat2, Neighborhood, by = "neighborhood_indicator2")
# Normalize population and residential units by area
dat2$pop_density <- dat2$POPULATION/ dat2$area
dat2$res_density <- dat2$RES_UNITS/ dat2$area
# instead just load pre-processed data
road_density <- read_csv("data/processed_data/road_density.csv")
dat2 <- left_join(dat2, road_density, by = "neighborhood_indicator2")
# Clean outcome space of variables ----------------------------------------------------------
dat3 <- dat2
# gender: [Q13_1]
dat3$gender <- ifelse(dat3$Q13_1 == 96, NA, dat3$Q13_1)
# age: [Q8_1] "How old are you?"
dat3$age <- ifelse(dat3$Q8_1 %in% c(98,96,99), NA, dat3$Q8_1)
# income:[Q13_7] "Which of these statements comes closest to describing your household
#income immediately before Daesh captured Mosul?"
dat3$income_pre_IS <- ifelse(dat3$Q13_7 %in% c(98,96,99), NA, dat3$Q13_7)
dat3$income_pre_IS_1 <- as.integer(dat3$income_pre_IS == 1)
dat3$income_pre_IS_2 <- as.integer(dat3$income_pre_IS == 2)
dat3$income_pre_IS_3 <- as.integer(dat3$income_pre_IS == 3)
dat3$income_pre_IS_4 <- as.integer(dat3$income_pre_IS == 4)
dat3$income_pre_IS_3_4 <- as.integer(dat3$income_pre_IS > 2)
# identity: [Q13_15] "Which of the following best describes you? Above all, I am …”
dat3$identity <- ifelse(dat3$Q13_15 %in% c(98,96,99), NA, dat3$Q13_15)
dat3$identity_1 <- as.integer(dat3$identity == 1)
dat3$identity_2 <- as.integer(dat3$identity == 2)
dat3$identity_3 <- as.integer(dat3$identity == 3)
dat3$identity_4 <- as.integer(dat3$identity == 4)
dat3$identity_5 <- as.integer(dat3$identity == 5)
dat3$identity_6 <- as.integer(dat3$identity == 6)
dat3$identity_7 <- as.integer(dat3$identity == 7)
# education: [Q8_2] "What is the highest level of education you have completed?"
dat3$education <- ifelse(dat3$Q8_2 %in% c(98,96,99), NA, dat3$Q8_2)
dat3$education_1 <- as.integer(dat3$education == 1)
dat3$education_2 <- as.integer(dat3$education == 2)
dat3$education_3 <- as.integer(dat3$education == 3)
dat3$education_4 <- as.integer(dat3$education == 4)
dat3$education_5 <- as.integer(dat3$education == 5)
dat3$education_6 <- as.integer(dat3$education == 6)
dat3$education_7 <- as.integer(dat3$education == 7)
dat3$education_4_5_6_7 <- as.integer(dat3$education > 3)
# vote: [Q13_12] "Did you vote in the last parliamentary election in 2014?"
dat3$vote <- ifelse(dat3$Q13_12 %in% c(98,96,99), NA, dat3$Q13_12)
# sharia [Q9_6] Do you believe that Iraqi state law should be reformed to include more Sharia, less Sharia, or stay the same as it is now?
dat3$sharia <- ifelse(dat3$Q9_6 %in% c(98,96,99), NA, dat3$Q9_6)
dat3$sharia_1 <- as.integer(dat3$sharia == 1)
dat3$sharia_2 <- as.integer(dat3$sharia == 2)
dat3$sharia_3 <- as.integer(dat3$sharia == 3)
# prayer: [Q13_10] How often do you attend Friday prayer?
dat3$prayer <- ifelse(dat3$Q13_10 %in% c(98,96,99), NA, dat3$Q13_10)
dat3$prayer_1 <- as.integer(dat3$prayer == 1)
dat3$prayer_2 <- as.integer(dat3$prayer == 2)
dat3$prayer_3 <- as.integer(dat3$prayer == 3)
dat3$prayer_4 <- as.integer(dat3$prayer == 4)
dat3$prayer_5 <- as.integer(dat3$prayer == 5)
dat3$prayer_4_5 <- as.integer(dat3$prayer > 3)
## Grievances with the Iraqi government (considering multiple related questions)
# "During the years 2006- 2014...
# [Q10_10] ... did you ever feel disrespected or insulted by an Iraqi police officer?"
dat3$gov_grievance_police <- ifelse(dat3$Q10_10 %in% c(98, 96, 99), NA, dat3$Q10_10)
# [Q10_11] "...were you ever arrested?"
dat3$gov_grievance_arrested <- ifelse(dat3$Q10_11 %in% c(98, 96, 99), NA, dat3$Q10_11)
# [Q10_12] "...did you ever feel that you
# were discriminated against by an Iraqi government employee (either
# military or civilian) for being Sunni?"
dat3$gov_grievance_sunni <- ifelse(dat3$Q10_12 %in% c(98, 96, 99), NA, dat3$Q10_12)
# [Q10_13] "...did you ever participate in a demonstration or protest directed at
# the Iraqi government?"
dat3$gov_grievance_protest <- ifelse(dat3$Q10_13 %in% c(98, 96, 99), NA, dat3$Q10_13)
# Combine grievances into one variable: 'gov_grievances'
dat3$gov_grievances <- rowSums(dat3[, c("gov_grievance_police", "gov_grievance_arrested", "gov_grievance_sunni", "gov_grievance_protest")], na.rm = TRUE)
# Handle missing values: If all grievance indicators are NA, set 'gov_grievances' to NA
dat3$gov_grievances <- ifelse(rowSums(is.na(dat3[, c("gov_grievance_police", "gov_grievance_arrested", "gov_grievance_sunni", "gov_grievance_protest")])) == 4, NA, dat3$gov_grievances)
## Harmed during Battle of Mosul (considering multiple related questions)
# Did your household experience any of the following harms during the battle for Mosul?
# house_damage: [Q12_4_1] Was the house or apartment that you were living in during the battle seriously damaged?
dat3$house_damage <- ifelse(dat3$Q12_4_1 %in% c(98,96,99), NA, dat3$Q12_4_1)
# hh_injured: [Q12_4_3] Was a member of your household injured?
dat3$hh_injured <- ifelse(dat3$Q12_4_3 %in% c(98,96,99), NA, dat3$Q12_4_3)
# hh_killed: [Q12_4_3] Was a member of your household killed?
dat3$hh_killed <- ifelse(dat3$Q12_4_4 %in% c(98,96,99), NA, dat3$Q12_4_4)
# Combine household harm into one variable: 'death_or_injury'
dat3$death_or_injury <- ifelse(dat3$hh_injured%in% 1| dat3$hh_killed%in% 1 , 1, 0)
dat3$death_or_injury <- ifelse(is.na(dat3$hh_injured) & is.na(dat3$hh_killed), NA, dat3$death_or_injury)
# Create binary version of satellite-detected damage
dat3$damage_in_10m_binary <- ifelse(dat3$damage_in_10m > 0, 1, dat3$damage_in_10m)
## Harmed by IS (considering multiple related questions)
# [Q11_21_1] "Was the house or apartment that you were living in during the time that Daesh was in
# control of Mosul seriously damaged?"
dat3$IS_house_damage<- ifelse(dat3$Q11_21_1 %in% c(98,96,99), NA, dat3$Q11_21_1)
# Q11_21_2] "Was the house or apartment that you were living in
# during the time that Daesh was in control of Mosul confiscated by Daesh?"
dat3$IS_house_confiscated<- ifelse(dat3$Q11_21_2 %in% c(98,96,99), NA, dat3$Q11_21_2)
# [Q11_21_3] "Was a member of your household injured?"
dat3$IS_injured<- ifelse(dat3$Q11_21_3 %in% c(98,96,99), NA, dat3$Q11_21_3)
# [Q11_21_4] "Was a member of this household killed?"
dat3$IS_killed<- ifelse(dat3$Q11_21_4 %in% c(98,96,99), NA, dat3$Q11_21_4)
# Combine harm into one variable: 'IS_any_harm'
dat3$IS_any_harm <- ifelse(dat3$IS_house_damage %in% 1 | dat3$IS_house_confiscated %in% 1 | dat3$IS_injured %in% 1
| dat3$IS_killed %in% 1, 1, 0)
# Handle missing values: If all harm indicators are NA, set 'IS_any_harm' to NA
dat3$IS_any_harm <- ifelse(is.na(dat3$IS_house_damage)&  is.na(dat3$IS_house_confiscated) & is.na(dat3$IS_injured) &
is.na(dat3$IS_killed) , NA, dat3$IS_any_harm)
# Combine blame for harm into one variable: 'IS_blame'
dat3$IS_blame <- ifelse(dat3$Q11_22 %in% 1| dat3$Q11_23 %in% 1|dat3$Q11_24 %in% 1, 1, 0)
# Handle missing values: If all harm indicators are NA, set 'IS_blame_IS' to NA
dat3$IS_blame <- ifelse(is.na(dat3$Q11_22)&  is.na(dat3$Q11_23) & is.na(dat3$Q11_24), NA, dat3$IS_blame)
## IS Service provisions (considering multiple related questions)
# "During the first six months of Daesh rule, did Daesh collect any of the following
# types of taxes and fees from this household?..."
# [Q11_16_1] Electricity fees
dat3$IS_electric <- ifelse(dat3$Q11_16_1 %in% c(98,96,99), NA, dat3$Q11_16_1)
# [Q11_16_2] Water fees
dat3$IS_water<- ifelse(dat3$Q11_16_2 %in% c(98,96,99), NA, dat3$Q11_16_2)
# [Q11_16_3] Zakat
dat3$IS_zakat <- ifelse(dat3$Q11_16_3 %in% c(98,96,99), NA, dat3$Q11_16_3)
# Combine service provisions into one variable: 'IS_services'
dat3$IS_services <- dat3$IS_electric + dat3$IS_water + dat3$IS_zakat
# Handle missing values: If all service indicators are NA, set 'IS_services' to NA
dat3$IS_services <- ifelse(is.na(dat3$IS_electric)&  is.na(dat3$IS_water) & is.na(dat3$IS_zakat), NA, dat3$IS_services)
# # Military Legitimacy  (considering multipleactors)
# "In your opinion, how likely are the following actors to kill innocent civilians?"
# [Q12_8_1] United States
dat3$us_legitimacy <- ifelse(dat3$Q12_8_1 %in% c(98,96,99), NA, dat3$Q12_8_1)
# [Q12_8_2] Iraqi Counter-Terrorism Service
dat3$cts_legitimacy <- ifelse(dat3$Q12_8_2 %in% c(98,96,99), NA, dat3$Q12_8_2)
# [Q12_8_3] Iraqi Army (Regular)
dat3$army_legitimacy <- ifelse(dat3$Q12_8_3 %in% c(98,96,99), NA, dat3$Q12_8_3)
# [Q12_8_4] Federarl Police
dat3$police_legitimacy <- ifelse(dat3$Q12_8_4 %in% c(98,96,99), NA, dat3$Q12_8_4)
# [Q12_8_5] PMF
dat3$pmf_legitimacy <- ifelse(dat3$Q12_8_5 %in% c(98,96,99), NA, dat3$Q12_8_5)
# reverse code the outcome space; perception that actor is less likely to kill civilians = higher legitimacy
dat3 <-
dat3 |>
mutate(
us_legitimacy = case_match(
us_legitimacy,
4 ~ 1,
3 ~ 2,
2 ~ 3,
1~ 4
),
cts_legitimacy = case_match(
cts_legitimacy,
4 ~ 1,
3 ~ 2,
2 ~ 3,
1~ 4
),
army_legitimacy = case_match(
army_legitimacy,
4 ~ 1,
3 ~ 2,
2 ~ 3,
1~ 4
),
police_legitimacy = case_match(
police_legitimacy,
4 ~ 1,
3 ~ 2,
2 ~ 3,
1~ 4
),
pmf_legitimacy = case_match(
pmf_legitimacy,
4 ~ 1,
3 ~ 2,
2 ~ 3,
1~ 4
)
)
# Combine legitimacy into one variable by taking mean across actors: 'military_legitimacy'
selected_columns <- dat3 %>% select(cts_legitimacy, police_legitimacy, army_legitimacy)
dat3$military_legitimacy <- rowMeans(selected_columns, na.rm = F)
## Looting(considering multiple actors)
# "Have you witnessed or heard about cases in which the following forces have stolen property or money
# (looting) from civilians in Mosul?"
# [Q12_9_1] Iraqi Counter-Terrorism
dat3$Q12_9_1 <- ifelse(dat3$Q12_9_1 %in% c(98,96,99), NA, dat3$Q12_9_1)
# [Q12_9_2] Iraqi Army (Regular)
dat3$Q12_9_2 <- ifelse(dat3$Q12_9_2 %in% c(98,96,99), NA, dat3$Q12_9_2)
# [Q12_9_3] Iraqi Federal Police
dat3$Q12_9_3 <- ifelse(dat3$Q12_9_3 %in% c(98,96,99), NA, dat3$Q12_9_3)
# Combine looting into one variable: 'looting'
dat3$looting <- ifelse(dat3$Q12_9_1%in% 1|dat3$Q12_9_2%in%1| dat3$Q12_9_3%in% 1, 1, 0)
## "Intermediate Variables"
# daesh_govern: [Q11_11] "During the first six months of Daesh rule, did you believe that Daesh
#was doing a better job of governing Mosul than the Iraqi government did previously?"
dat3$daesh_govern <- ifelse(dat3$Q11_11 %in% c(98,96,99), NA, dat3$Q11_11)
# daesh_corrupt: [Q11_12] "During the first six months of Daesh rule, to what extent did you think
# that Daesh was corrupt?"
dat3$daesh_corrupt <- ifelse(dat3$Q11_12 %in% c(98,96,99), NA, dat3$Q11_12)
# daesh_taxes: [Q11_17] "How much do you agree or disagree with the following statement?
#`The taxes and fees collected by Daesh were fair in exchange for the services that Daesh was providing.`''
dat3$daesh_taxes <- ifelse(dat3$Q11_17 %in% c(98,96,99), NA, dat3$Q11_17)
# daesh_school: [Q11_20] "Did any of the children in this household attend schools in Mosul
#while Daesh controlled the city?"
dat3$daesh_school <- ifelse(dat3$Q11_20 %in% c(98,96,99), NA, dat3$Q11_20)
# corrupt_current: [Q10_4] "In general, to what extent do you think that the current Iraqi government is corrupt?"
dat3$corrupt_current <- ifelse(dat3$Q10_4 %in% c(98,96,99), NA, dat3$Q10_4)
# Subset and Export ----------------------------------------------------------
# subset variables for analysis
dat4 <- dat3 %>%
select(SbjNum, Neighborhood, Latitude, Longitude,
gender, age,
income_pre_IS, income_pre_IS_1, income_pre_IS_2, income_pre_IS_3, income_pre_IS_4, income_pre_IS_3_4,
identity, identity_1, identity_2,identity_3, identity_4, identity_5, identity_6, identity_7,
education, education_1, education_2, education_3, education_4, education_5, education_6, education_7, education_4_5_6_7,
vote,
treated,
cts_legitimacy, police_legitimacy, army_legitimacy, military_legitimacy, us_legitimacy, pmf_legitimacy,
house_damage, hh_injured, hh_killed, death_or_injury,
damage_in_10m, damage_in_10m_binary, damage_in_50m, damage_in_100m,damage_in_500m,
gov_grievances, gov_grievance_police, gov_grievance_arrested, gov_grievance_sunni, gov_grievance_protest,
IS_any_harm, IS_electric, IS_water, IS_zakat,
sharia, sharia_1, sharia_2, sharia_3,
prayer, prayer_1, prayer_2, prayer_3, prayer_4, prayer_5, prayer_4_5,
daesh_govern, daesh_corrupt, daesh_taxes, corrupt_current,
looting,
pop_density, road_density, res_density)
write_dta(dat4, "data/clean_survey.dta")
# Clean Health Survey (Lafta et al. 2018)  ---------------------------------------------------------
data <-read_excel("data/raw_data/mosul_deaths_injuries_min_dataset_21jan2018.xls")
# limit the sample to Death and Injury reported during the battle of mosul
data <- data %>%
filter(!is.na(personmonths_lib))
# create cause of death variables
# any
data$death <- ifelse(data$death_violent_event %in% "Yes", 1,0)
# airstrike
data$death_airstrike <- ifelse(data$death_cause_conflict %in% "Airstrike", 1,0)
# explosion
data$death_explosions <- ifelse(data$death_cause_conflict %in% "Other explosions", 1,0)
# gunshot
data$death_gunshot <- ifelse(data$death_cause_conflict %in% "Gunshot", 1,0)
# car bomb
data$death_carbomb <- ifelse(data$death_cause_conflict %in% "Car bomb" , 1,0)
# other intentional
data$death_other_conflict <- ifelse(data$death_cause_conflict %in% "Other conflict-related injury" , 1,0)
# export
export <- data %>%
select(side, death, death_airstrike, death_explosions, death_gunshot, death_carbomb, death_other_conflict)
write_dta(export, "data/processed_data/health_survey.dta")
